U.S. patent number 10,349,219 [Application Number 15/416,852] was granted by the patent office on 2019-07-09 for methods and systems for combining sensor data to determine vehicle movement information.
This patent grant is currently assigned to TRUEMOTION, INC.. The grantee listed for this patent is TRUEMOTION, INC.. Invention is credited to Nicholas Arcolano, Brad Cordova, Sanujit Sahoo.
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United States Patent |
10,349,219 |
Cordova , et al. |
July 9, 2019 |
Methods and systems for combining sensor data to determine vehicle
movement information
Abstract
According to some embodiments of the invention, movement
measurements may be obtained from a movement sensor (e.g., an
accelerometer) of a mobile device in a vehicle. In addition,
location measurements may be obtained from a location sensor (e.g.,
a GPS) of the mobile device in the vehicle. The movement
measurements and the location measurements may be cross-referenced
to each other to remove erroneous measurements, such as physically
impossible measurements. The remaining measurements may be used to
draw conclusions about the movements or locations, such as to
identify a movement event (e.g., a braking event, an acceleration
event, or the like).
Inventors: |
Cordova; Brad (Cambridge,
MA), Sahoo; Sanujit (Cambridge, MA), Arcolano;
Nicholas (Cambridge, MA) |
Applicant: |
Name |
City |
State |
Country |
Type |
TRUEMOTION, INC. |
Boston |
MA |
US |
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Assignee: |
TRUEMOTION, INC. (Boston,
MA)
|
Family
ID: |
59360359 |
Appl.
No.: |
15/416,852 |
Filed: |
January 26, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170211939 A1 |
Jul 27, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62287262 |
Jan 26, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01C
21/28 (20130101); H04W 4/029 (20180201); G01C
21/165 (20130101); H04W 4/027 (20130101); G01S
19/40 (20130101); G01S 19/52 (20130101) |
Current International
Class: |
H04W
4/02 (20180101); G01C 21/28 (20060101); H04W
4/029 (20180101); G01C 21/16 (20060101); G01S
19/40 (20100101); G01S 19/52 (20100101) |
Field of
Search: |
;701/509 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Black; Thomas G
Assistant Examiner: Huynh; Luat T
Attorney, Agent or Firm: Kilpatrick Townsend & Stockton
LLP
Parent Case Text
CROSS-REFERENCES TO RELATED APPLICATIONS
This application claims the benefit of U.S. Provisional Patent
Application No. 62/287,262, entitled "METHODS AND SYSTEMS FOR
COMBINING SENSOR DATA TO DETERMINE VEHICLE MOVEMENT INFORMATION",
filed Jan. 26, 2016, the content of which is hereby incorporated by
reference in its entirety.
Claims
What is claimed is:
1. A method comprising: operating a movement sensor of a mobile
device disposed in a vehicle to obtain a plurality of movement
measurements; operating a location sensor of the mobile device
disposed in the vehicle to obtain a plurality of location
measurements; verifying, by a processor of the mobile device, a
portion of the plurality of movement measurements using the
plurality of location measurements; removing, by the processor, one
or more movement measurements that are not verified from the
plurality of movement measurements to provide a set of remaining
movement measurements; and determining, by the processor, a
movement event for the vehicle using the set of remaining movement
measurements.
2. The method of claim 1, further comprising: using two or more
approaches to verifying the portion of the plurality of movement
measurements, wherein verifying using the plurality of location
measurements is used by at least one of the approaches, wherein
removing the one or more movement measurements that are not
verified from the portion of the plurality of movement measurements
to provide the set of remaining movement measurements comprises,
for the plurality of movement measurements, independently using
each of the approaches to remove the one or more movement
measurements that are not verified.
3. The method of claim 1, wherein verifying the portion of the
plurality of movement measurements comprises: determining, by the
processor, a movement measurement of a first portion of the
plurality of movement measurements as not verified based on a
comparison of an accuracy estimate of the movement measurement and
a first threshold.
4. The method of claim 1, wherein verifying the portion of the
plurality of movement measurements comprises: applying, by the
processor, a first set of rules to two movement measurements of a
second portion of the plurality of movement measurements to
determine that the two movement measurements are not both
physically possible; and determining, by the processor, one of the
two movement measurements determined to not be physically possible
as not verified.
5. The method of claim 1, wherein verifying the portion of the
plurality of movement measurements comprises: determining, by the
processor, a third portion of the plurality of movement
measurements having duplicate values as not verified.
6. The method of claim 5, wherein the duplicate values of the third
portion of the plurality of movement measurements are time
values.
7. The method of claim 5, wherein the duplicate values of the third
portion of the plurality of movement measurements are location
values.
8. The method of claim 1, wherein verifying the portion of the
plurality of movement measurements comprises: applying, by the
processor, a second set of rules to a fourth portion of the
plurality of movement measurements to determine that each movement
measurement of the fourth portion of the plurality of movement
measurements has location values that are clustered; and
determining, by the processor, the fourth portion of the plurality
of movement measurements as not verified.
9. The method of claim 1, further comprising: grouping each of the
set of remaining movement measurements into groups based on a
collection time for the set of remaining movement measurements, the
grouping resulting in a plurality of movement measurement groups,
wherein the set of remaining movement measurements each have a
speed component; for a first group of the plurality of movement
measurement groups, using the speed component of each movement
measurement of the first group to determine a plurality of
acceleration rates describing changes in the speed component;
applying a second threshold to the acceleration rates; and removing
two or more movement measurements from the first group based on the
second threshold being applied to the acceleration rates associated
with the two or more movement measurements.
10. A device comprising: a memory; and a processor coupled to the
memory, wherein the processor is configured to perform operations
including: operating a movement sensor of a mobile device disposed
in a vehicle to obtain a plurality of movement measurements;
operating a location sensor of the mobile device disposed in the
vehicle to obtain a plurality of location measurements; verifying a
portion of the plurality of movement measurements using the
plurality of location measurements; removing one or more movement
measurements that are not verified from the plurality of movement
measurements to provide a set of remaining movement measurements;
and determining a movement event for the vehicle using the set of
remaining movement measurements.
11. The device of claim 10, wherein the processor is further
configured to: use two or more approaches to verifying the portion
of the plurality of movement measurements, wherein verifying using
the plurality of location measurements is used by at least one of
the approaches, wherein removing the one or more movement
measurements that are not verified from the plurality of movement
measurements to provide the set of remaining movement measurements
comprises, for the plurality of movement measurements,
independently using each of the approaches to remove the one or
more movement measurements that are not verified.
12. The device of claim 10, wherein verifying the portion of the
plurality of movement measurements comprises: determining a
movement measurement of a first portion of the plurality of
movement measurements as not verified based on a comparison of an
accuracy estimate of the movement measurement and a first
threshold.
13. The device of claim 10, wherein verifying the portion of the
plurality of movement measurements comprises: applying a first set
of rules to two movement measurements of a second portion of the
plurality of movement measurements to determine that the two
movement measurements are not both physically possible; and
determining one of the two movement measurements determined to not
be physically possible as not verified.
14. The device of claim 10, wherein verifying the portion of the
plurality of movement measurements comprises: determining a third
portion of the plurality of movement measurements having duplicate
values as not verified.
15. The device of claim 14, wherein the duplicate values of the
third portion of the plurality of movement measurements are time
values.
16. The device of claim 14, wherein the duplicate values of the
third portion of the plurality of movement measurements are
location values.
17. The device of claim 10, wherein verifying the portion of the
plurality of movement measurements comprises: applying a second set
of rules to a fourth portion of the plurality of movement
measurements to determine that each movement measurement of the
fourth portion of the plurality of movement measurements has
location values that are clustered; and determining the fourth
portion of the plurality of movement measurements as not
verified.
18. The device of claim 10, wherein the processor is further
configured to: group each of the set of remaining movement
measurements into groups based on a collection time for the set of
remaining movement measurements, the grouping resulting in a
plurality of movement measurement groups, wherein the set of
remaining movement measurements have a speed component; for a first
group of the plurality of movement measurement groups, use the
speed component of each movement measurement of the first group to
determine a plurality of acceleration rates describing changes in
the speed component; apply a second threshold to the acceleration
rates; and remove two or more movement measurements from the first
group based on the second threshold being applied to the
acceleration rates associated with the two or more movement
measurements.
Description
BACKGROUND OF THE INVENTION
Mobile devices, including smart phones, have been utilized to
provide location information to users. Mobile devices can use a
number of different techniques to produce location data. One
example is the use of Global Positioning System (GPS) chipsets,
which are now widely available, to produce location information for
a mobile device. Some systems have been developed to track driving
behaviors including speed, braking, and turn speed. Such systems
include external devices that have been physically integrated with
vehicles to track driving behavior.
SUMMARY OF THE INVENTION
Despite the progress made in relation to collecting data related to
drivers and their driving behavior, there is a need in the art for
improved systems and methods related to combining sensor data to
determine vehicle movement information.
According to some embodiments of the invention, movement
measurements may be obtained from a movement sensor of a mobile
device in a vehicle. In addition, location measurements may be
obtained from a location sensor of the mobile device in the
vehicle. The movement measurements and the location measurements
may be cross-referenced to each other to remove erroneous
measurements, such as physically impossible measurements. The
remaining measurements may be used to draw conclusions about the
movements or locations, such as to identify a movement event (e.g.,
a braking event, an acceleration event, etc.).
According to some embodiments of the invention, a method of
determining movement events for a vehicle is provided. The method
comprises obtaining a plurality of movement measurements from a
movement sensor of a mobile device disposed in a vehicle. The
method further comprises obtaining a plurality of location
measurements from a location sensor of the mobile device disposed
in the vehicle. The method further comprises verifying the
plurality of movement measurements using the plurality of location
measurements. The method further comprises removing one or more
movement measurements from the plurality of movement measurements
using the plurality of location measurements to provide a set of
remaining movement measurements. The method further comprises
determining a movement event for the vehicle using the remaining
movement measurements.
According to some embodiments of the invention, a device is
provided. The device comprises a memory. The device further
comprises a processor. The processor is configured to perform
operations including the steps of the above method.
According to some embodiments, a computer-program product is
provided. The computer-program product is tangibly embodied in a
non-transitory machine-readable storage medium of a device. The
computer-program product includes instructions that, when executed
by one or more processors, cause the one or more processors to
perform operations including the steps of the above method.
This summary is not intended to identify key or essential features
of the claimed subject matter, nor is it intended to be used in
isolation to determine the scope of the claimed subject matter. The
subject matter should be understood by reference to appropriate
portions of the entire specification of this patent, any or all
drawings, and each claim.
The foregoing, together with other features and embodiments, will
become more apparent upon referring to the following specification,
claims, and accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
Illustrative embodiments of the present invention are described in
detail below with reference to the following drawing figures:
FIG. 1 is a system diagram illustrating a mobile device according
to an embodiment of the invention.
FIG. 2 is a system diagram illustrating a server according to an
embodiment of the invention.
FIG. 3 is a plot illustrating raw and processed speed measurements
according to an embodiment of the invention.
FIG. 4 is a table illustrating exemplary movement measurements
according to an embodiment of the invention.
FIGS. 5A-5C are graphs illustrating transformations o data
resulting from the processes of FIG. 4 according to an embodiment
of the invention.
FIG. 6 is a graphical user interface (GUI) illustrating an
exemplary drive on a map according to an embodiment of the
invention.
FIG. 7 is a flow chart illustrating a method of analyzing movement
measurements according to an embodiment of the invention.
DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
Certain aspects and embodiments of this disclosure are provided
below. Some of these aspects and embodiments may be applied
independently and some of them may be applied in combination as
would be apparent to those of skill in the art. In the following
description, for the purposes of explanation, specific details are
set forth in order to provide a thorough understanding of
embodiments of the invention. However, it will be apparent that
various embodiments may be practiced without these specific
details. The figures and description are not intended to be
restrictive.
The ensuing description provides exemplary embodiments only, and is
not intended to limit the scope, applicability, or configuration of
the disclosure. Rather, the ensuing description of the exemplary
embodiments will provide those skilled in the art with an enabling
description for implementing an exemplary embodiment. It should be
understood that various changes may be made in the function and
arrangement of elements without departing from the spirit and scope
of the invention as set forth in the appended claims.
Specific details are given in the following description to provide
a thorough understanding of the embodiments. However, it will be
understood by one of ordinary skill in the art that the embodiments
may be practiced without these specific details. For example,
circuits, systems, networks, processes, and other components may be
shown as components in block diagram form in order not to obscure
the embodiments in unnecessary detail. In other instances,
well-known circuits, processes, algorithms, structures, and
techniques may be shown without unnecessary detail in order to
avoid obscuring the embodiments.
Also, it is noted that individual embodiments may be described as a
process which is depicted as a flowchart, a flow diagram, a data
flow diagram, a structure diagram, or a block diagram. Although a
flowchart may describe the operations as a sequential process, many
of the operations can be performed in parallel or concurrently. In
addition, the order of the operations may be re-arranged. A process
is terminated when its operations are completed, but could have
additional steps not included in a figure. A process may correspond
to a method, a function, a procedure, a subroutine, a subprogram,
etc. When a process corresponds to a function, its termination can
correspond to a return of the function to the calling function or
the main function.
The term "computer-readable medium" includes, but is not limited
to, portable or non-portable storage devices, optical storage
devices, and various other mediums capable of storing, containing,
or carrying instruction(s) and/or data. A computer-readable medium
may include a non-transitory medium in which data can be stored and
that does not include carrier waves and/or transitory electronic
signals propagating wirelessly or over wired connections. Examples
of a non-transitory medium may include, but are not limited to, a
magnetic disk or tape, optical storage media such as compact disk
(CD) or digital versatile disk (DVD), flash memory, memory or
memory devices. A computer-readable medium may have stored thereon
code and/or machine-executable instructions that may represent a
procedure, a function, a subprogram, a program, a routine, a
subroutine, a module, a software package, a class, or any
combination of instructions, data structures, or program
statements. A code segment may be coupled to another code segment
or a hardware circuit by passing and/or receiving information,
data, arguments, parameters, or memory contents. Information,
arguments, parameters, data, etc. may be passed, forwarded, or
transmitted via any suitable means including memory sharing,
message passing, token passing, network transmission, or the
like.
Furthermore, embodiments may be implemented by hardware, software,
firmware, middleware, microcode, hardware description languages, or
any combination thereof. When implemented in software, firmware,
middleware or microcode, the program code or code segments to
perform the necessary tasks (e.g., a computer-program product) may
be stored in a computer-readable or machine-readable medium. A
processor(s) may perform the necessary tasks.
As discussed below, some embodiments described herein use
approaches to collecting and analyzing driving data similar to the
approaches described in U.S. patent application Ser. No.
15/149,603, filed May 9, 2016, entitled "METHODS AND SYSTEMS FOR
SENSOR-BASED VEHICLE ACCELERATION DETERMINATION," (the '603
Application), U.S. patent application Ser. No. 15/149,613, filed
May 9, 2016, entitled "METHODS AND SYSTEMS FOR DRIVING DATA
COLLECTION" (the '613 Application), U.S. patent application Ser.
No. 14/749,232, filed Jun. 24, 2015, entitled "METHODS AND SYSTEMS
FOR ALIGNING A MOBILE DEVICE TO A VEHICLE" (the '232 Application),
and U.S. Provisional patent application. Ser. No. 15/249,967, filed
Aug. 29, 2016, entitled "METHODS AND SYSTEMS FOR PRESENTING
COLLECTED DRIVING DATA," (the '967 Application) ("the Incorporated
Applications"). These applications are incorporated by reference
herein in their entirety for all purposes.
Specific examples of the use of different embodiments disclosed in
these applications ("the Incorporated Applications") are provided
herein, and one having skill in the relevant art(s), will
appreciate how additional approaches described in these
applications can be used by some embodiments described herein.
FIG. 1 is a system diagram illustrating a system 100 for collecting
driving data according to an embodiment of the present invention.
System 100 may include a mobile device 101 having a number of
different components. Mobile device 101 may include a sensor data
block 105, a data processing block 120, and a data transmission
block 130. The sensor data block 105 may include data collection
sensors as well as data collected from these sensors that are
available to mobile device 101. This can include external devices
connected via Bluetooth, USB cable, etc. The data processing block
120 may include storage 126 for manipulations done to the data
obtained from the sensor data block 105 by processor 122 and memory
124 coupled to the processor 122. This may include, but is not
limited to, analyzing, characterizing, subsampling, filtering,
reformatting, etc. Data transmission block 130 may include any
transmission of the data off the phone to an external computing
device that can also store and manipulate the data obtained from
sensor data block 105, such as by using a wireless transceiver 132
a cellular transceiver 134, and/or direct transmission (e.g.,
through a cable or other wired connection). The external computing
device can be, for example, a server 150. Server 150 can comprise
its own processor 152 and storage 156.
Some embodiments of the present invention are described using
examples where driving data is collected using a mobile device 101,
and these examples are not limited to any particular mobile device.
As examples, a variety of mobile devices including sensors such as
GPS receivers 110, accelerometers 112, gyroscopes 116,
magnetometers 114, microphones 118, compasses 119, barometers 113,
location determination systems such as global positioning system
(GPS) receivers 110, communications capabilities, and the like are
included within the scope of some embodiments. Exemplary mobile
devices include smart watches, wearable devices, fitness monitors,
Bluetooth headsets, tablets, laptop computers, smart phones, music
players, movement analysis devices, and other suitable devices. One
of ordinary skill in the art, given the description herein, would
recognize many variations, modifications, and alternatives for the
implementation of embodiments.
To collect data associated with the driving behavior of a driver,
one or more sensors on mobile device 101 (e.g., the sensors of
sensor data block 105) may be operated close in time to a period
when mobile device 101 is with the driver when operating a
vehicle--also termed herein "a drive" or "a trip". With many mobile
devices 101, the sensors used to collect data are components of the
mobile device 101, and use power resources available to mobile
device 101 components, e.g., mobile device battery power and/or a
power source external to mobile device 101.
Some embodiments use settings of a mobile device to enable
different functions described herein. For example, in Apple iOS,
and/or Android OS, having certain settings enabled can enable
certain functions of embodiments. For some embodiments, having
location services enabled allows the collection of location
information from the mobile device (e.g., collected by global
positioning system (GPS) sensors), and enabling background app
refresh allows some embodiments to execute in the background,
collecting and analyzing driving data even when the application is
not executing.
FIG. 2 shows a system 200 for collecting driving data that can
include a server 201 that communicates with mobile device 101. In
some embodiments, server 201 may provide functionality using
components including, but not limited to vector analyzer 258,
vector determiner 259, external information receiver 212,
classifier 214, data collection frequency engine 252, driver
detection engine 254, and scoring engine 290. These components are
executed by processors (not shown) in conjunction with memory (not
shown). Server 201 may also include data storage 256. It is
important to note that, while not shown, one or more of the
components shown operating within server 201 can operate fully or
partially within mobile device 101, and vice versa.
To collect data associated with the driving behavior of a driver,
one or more sensors on mobile device 101 (e.g., the sensors of
sensor data block 105) may be operated close in time to a period
when mobile device 101 is with the driver when operating a
vehicle--also termed herein "a drive" or "a trip". Once the mobile
device sensors have collected data (and/or in real time), some
embodiments analyze the data to determine acceleration vectors for
the vehicle, as well as different features of the drive. Exemplary
processes detect and classify driving features using classifier
214, and determine acceleration vectors using vector analyzer 258
and vector determiner 259. In some embodiments, external data
(e.g., weather) can be retrieved and correlated with collected
driving data.
As discussed herein, some embodiments can transform collected
sensor data (e.g., driving data collected using sensor data block
105) into different results to analyze movement measurements and to
detect the occurrence of driving events. Although shown and
described as being contained within server 201, it is contemplated
that any or all of the components of server 201 may instead be
implemented within mobile device 101, and vice versa. It is further
contemplated that any or all of the functionalities described
herein may be performed during a drive, in real time, or after a
drive.
FIG. 3 is a chart 300 showing a plot of collected "raw" speed 250
measurements 260 of a vehicle over a period of time 255, and a plot
of speed measurements over time after the measurements have been
processed 270. Some examples of processing different collected
values are discussed below with respect to FIGS. 4-7. It should be
noted that FIG. 3 is an example of results from some embodiments,
and that other results may occur.
In some embodiments, one of the movement measurements collected by
a mobile device (e.g., by GPS receiver 110 of mobile device 101 of
FIG. 1) is the speed of a vehicle as determined from a speed
source, e.g., GPS measurements. However, it is contemplated that
GPS measurements may provide erroneous data. Some embodiments use
one or more of the following approaches to remove erroneous speed
data from a data set.
The raw GPS speed includes spikes 260A and 260B that may indicate
large accelerations 260A or decelerations 260B. The processing of
the GPS data removes these deviation events providing the smoothed
profile illustrated by curve 270. Examples of erroneous GPS data
that may be collected is provided below.
GPS Signal Inaccuracy:
Some GPS receivers provide a level of accuracy for provided GPS
measurements. In some embodiments, a threshold can be applied to
these measurements, and those with a confidence level below a
threshold can be removed from a data set of measurements.
Time Duplicates:
Some GPS receivers can provide erroneous data in the form of
multiple GPS measurements for the same time value, e.g., 20 mph
measured at 5:01 minutes, and 5 mph received for the same time. In
some embodiments, a stream of location measurements can be analyzed
for multiple values of this type, and either all of the duplicate
values can be removed or one of the values can be selected to be
removed using some approach, e.g., the value closest to the
previous time value, the point closest to the center of the
cluster, the point closest to a previously measured point that is
outside the cluster, and/or the like.
Clusters of Points Over a Small Duration of Time:
Some GPS receivers can provide erroneous data that is a cluster of
points in a small duration of time. In some embodiments, a stream
of location measurements can be analyzed for this problem, e.g., by
measuring the distance between points and comparing to the amount
of time between the measurements. When these clusters are detected,
either all the duplicate values can be removed or one of the values
can be selected using some approach, e.g., the point closest to the
center of the cluster, the point closest to a previously measured
point that is outside the cluster, and/or the like.
Speed and/or Time Measurements Beyond Physics:
Some GPS receivers can provide erroneous data that are points that
are impossible based on the characteristics of vehicle movement and
the laws of physics. In some embodiments, a stream of location
measurements can be analyzed for this problem, e.g., by comparing
points and applying rules to detect physically impossible
measurements. For example, a measurement of 20 mph followed seconds
later by two 4 mph measurements, then a measurement of 15 mph a
second later may be physically impossible. When these types of
measurements are detected, either all the values can be removed or
one of the values can be selected using some approach.
GPS "Sticky" Values:
Some GPS receivers can provide erroneous data that are a series of
repeated ("sticky") values over a small duration of time. In some
embodiments, a stream of location measurements can be analyzed for
this problem, e.g., by looking for sets of repeated values, and
applying thresholds to determine when repeated values are
potentially erroneous. When these repeated values are detected,
either all the duplicate values can be removed or one of the values
can be selected using some approach, e.g., the point closest to the
center of the cluster, the point closest to a previously measured
point that is outside the cluster, and/or the like.
FIG. 4 is a table that shows exemplary movement measurements
collected over time 450A. The column shown with event 452A has a
"B" to indicate that an example event has been detected based on
the time 450A data collected. In some embodiments, the occurrence
of certain events (e.g., hard braking, hard turning, speeding,
using a phone while driving, and/or the like) is indicative of
driving behavior by a driver that is relevant to a risk model for
the driver. Based on an analysis of diving data with respect to
certain events, events can give an indication of driver ability in
these areas. As discussed in the Incorporated Applications, in some
embodiments, a driver score is determined, for example, by the
scoring engine 290, based on analyzing detected events.
It should be appreciated that the example events shown in FIG. 4
have been simplified to demonstrate processing performed by some
embodiments, for example, some embodiments use multiple
measurements collected over a range of times to detect events. As
an illustration of this, when viewing table T1, time 450A rows can
be interpreted as multiple measurements over a range of time, not
just a single measurement.
In this example, without processing to detect potentially erroneous
measurements, nine braking events (B) would be reported for the
time 450A time range 1-25, e.g., 2, 4, 5, 6, 8, 12, 17, 20 and 21
listed in event 452A. In example embodiments, as shown by an "X"
one of more of processes 455A-455C are applied to estimate that
certain data points over time 450A are erroneous. For example,
process 455A detects, over time 450A, potential errors at 3, 4, 5,
10, 11, 12, 23, and 24.
In some embodiments, processes 455A-455C are one or more of the
approaches used to remove erroneous data described above with FIG.
3. It should be noted that additional approaches can be used by
embodiments, as well as that the number of approaches applied can
exceed the example shown in FIG. 4.
In some embodiments, measurements collected can be combined into
groups based on the application of one or more processes 455A-455C.
As shown in FIG. 4, some embodiments can create groups based on the
use of data points not determined by processes 455A-455C as
potentially erroneous. As shown in FIG. 4, based on using processes
455A-455C and the exclusion of measurements marked as potentially
erroneous ("X"), six groups of measurements are created--groups
490A values G1-G6. Thus, as shown in table T2, instead of the nine
"B" events, the groups 490B result in three events 452B (time 450B
values 2, 8 and 17).
FIGS. 5A-5C show how the data in groups (e.g., G1-G6) resulting
from the processes of FIG. 4 can be further transformed by some
embodiments. For example, once grouped, the measurement data
collected can be processed and additional results can be
determined. For example, multiple speed measurements can be
analyzed and transformed into acceleration values. In FIG. 5A,
collected measurements 510 (starting at time 515 and ending at time
517) can be grouped by the processes described in FIG. 4 into
groups 520A-520D. In some embodiments, graph line 550 of group 520A
(and similar lines shown for groups 520B-520D), represent
acceleration values determined from the measurements 510, after
processing by the approaches shown in FIG. 4.
FIG. 5B is a more detailed view of group 520A, with a graph line
555 having points 557A and 557B. In some embodiments, the
determined values in group 520A (e.g., acceleration values) can be
analyzed and potentially erroneous values can be removed. One
having skill in the relevant art(s), given the description herein,
will appreciate that different approaches can be used by
embodiments to detect potentially erroneous values. The approach
shown in FIGS. 5B and 5C marks data in group 520A as potentially
erroneous if the change in value exceeds a threshold. Applying a
threshold, points 557A and 557B are marked as potentially erroneous
by the approach.
Once marked as erroneous by the process of FIG. 5B, in FIG. 5C,
different approaches can be used to remove and/or replace values
marked as erroneous. For example, in FIG. 5C, lines 540A and 540B
show how a smoothing function can be applied by some embodiments to
replace points 557A-557B.
It should be appreciated that, by applying the two-stage analysis
described in FIGS. 4 and 5A-5C, some embodiments can improve the
performance of a computer system processing collected movement
measurement data, and improve the data produced by movement
sensors.
FIG. 6 depicts an exemplary drive from points 605 to 606 as shown
in an example graphical user interface (GUI) of an application.
Illustrating the grouping discussed with FIGS. 4 and 5A-5C above,
FIG. 6 shows data collected over a geographic area 640 with a drive
starting at 605 and ending at 606. On the depicted drive, potential
events 610A and 610B are shown, and groups 660A-660B of points. In
some embodiments, groups 660A-660B are created by processed
described with FIGS. 4 and 5A-5C above.
In this example, because event 610B is shown outside of a group,
only event 610A would be used by an embodiment, e.g., event 610B
would not be shown on a GUI as depicted in FIG. 6, and only one
"Hard Brake" would be counted in display value 650.
FIG. 7 is a simplified flowchart of the capture, analysis and use
of movement measurements associated with a driver, according to an
embodiment. The method described in FIG. 7 can use, for example,
approaches described herein and in the Incorporated Applications to
detect and measure the movement of a moving vehicle.
Method 700 begins as block 710 where a plurality of movement
measurements are obtained from a movement sensor of a mobile device
in a vehicle. In some embodiments, a plurality of movement
measurements (e.g., those shown with time 450A) are obtained from a
movement sensor (e.g., accelerometer 112) of a mobile device (e.g.,
mobile device 101) in a vehicle.
In block 720, a plurality of location measurements are obtained
from a location sensor of the mobile device in the vehicle. In some
embodiments, a plurality of location measurements is obtained from
a location sensor (e.g., data collected by GPS receiver 110) of the
mobile device in the vehicle.
In block 730, a movement measurement of the plurality of movement
measurements is verified using a portion of the plurality of
location measurements. In some embodiments, a movement measurement
of the plurality of movement measurements is verified (e.g., by
processor 180 performing process 455A) using a portion of the
plurality of location measurements.
In block 745, if the movement measurement is not verified, then the
movement measurement is removed from use to determine a movement
event for the vehicle. In some embodiments, if the movement
measurement is not verified (e.g., process 455A at time 450A
20-21), then the movement measurement is removed (e.g., none of
groups 490A G1-G6 include values from time 450A 20-21) from use to
determine a movement event for the vehicle.
In block 760, when the movement measurement is verified, then the
movement measurement is used to determine a movement event for the
vehicle. In some embodiments, when the movement measurement is
verified (e.g., time 450A 17 has no "X" marks), then the movement
measurement is used to determine a movement event for the vehicle
(e.g., a braking event is shown for time 450A 17 in group 490A
G4).
As noted, the computer-readable medium may include transient media,
such as a wireless broadcast or wired network transmission, or
storage media (that is, non-transitory storage media), such as a
hard disk, flash drive, compact disc, digital video disc, Blu-ray
disc, or other computer-readable media. The computer-readable
medium may be understood to include one or more computer-readable
media of various forms, in various examples.
In the foregoing description, aspects of the application are
described with reference to specific embodiments thereof, but those
skilled in the art will recognize that the invention is not limited
thereto. Thus, while illustrative embodiments of the application
have been described in detail herein, it is to be understood that
the inventive concepts may be otherwise variously embodied and
employed, and that the appended claims are intended to be construed
to include such variations, except as limited by the prior art.
Various features and aspects of the above-described invention may
be used individually or jointly. Further, embodiments can be
utilized in any number of environments and applications beyond
those described herein without departing from the broader spirit
and scope of the specification. The specification and drawings are,
accordingly, to be regarded as illustrative rather than
restrictive. For the purposes of illustration, methods were
described in a particular order. It should be appreciated that in
alternate embodiments, the methods may be performed in a different
order than that described.
Where components are described as performing or being "configured
to" perform certain operations, such configuration can be
accomplished, for example, by designing electronic circuits or
other hardware to perform the operation, by programming
programmable electronic circuits (e.g., microprocessors, or other
suitable electronic circuits) to perform the operation, or any
combination thereof.
The various illustrative logical blocks, modules, circuits, and
algorithm steps described in connection with the embodiments
disclosed herein may be implemented as electronic hardware,
computer software, firmware, or combinations thereof. To clearly
illustrate this interchangeability of hardware and software,
various illustrative components, blocks, modules, circuits, and
steps have been described above generally in terms of their
functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present invention.
The techniques described herein may also be implemented in
electronic hardware, computer software, firmware, or any
combination thereof. Such techniques may be implemented in any of a
variety of devices such as general purposes computers, wireless
communication device handsets, or integrated circuit devices having
multiple uses including application in wireless communication
device handsets and other devices. Any features described as
modules or components may be implemented together in an integrated
logic device or separately as discrete but interoperable logic
devices. If implemented in software, the techniques may be realized
at least in part by a computer-readable data storage medium
comprising program code including instructions that, when executed,
performs one or more of the methods described above. The
computer-readable data storage medium may form part of a computer
program product, which may include packaging materials. The
computer-readable medium may comprise memory or data storage media,
such as random access memory (RAM) such as synchronous dynamic
random access memory (SDRAM), read-only memory (ROM), non-volatile
random access memory (NVRAM), electrically erasable programmable
read-only memory (EEPROM), FLASH memory, magnetic or optical data
storage media, and the like. The techniques additionally, or
alternatively, may be realized at least in part by a
computer-readable communication medium that carries or communicates
program code in the form of instructions or data structures and
that can be accessed, read, and/or executed by a computer, such as
propagated signals or waves.
The program code may be executed by a processor, which may include
one or more processors, such as one or more digital signal
processors (DSPs), general purpose microprocessors, an application
specific integrated circuits (ASICs), field programmable logic
arrays (FPGAs), or other equivalent integrated or discrete logic
circuitry. Such a processor may be configured to perform any of the
techniques described in this disclosure. A general purpose
processor may be a microprocessor; but in the alternative, the
processor may be any conventional processor, controller,
microcontroller, or state machine. A processor may also be
implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration. Accordingly, the term
"processor," as used herein may refer to any of the foregoing
structure, any combination of the foregoing structure, or any other
structure or apparatus suitable for implementation of the
techniques described herein. In addition, in some aspects, the
functionality described herein may be provided within dedicated
software modules or hardware modules configured for encoding and
decoding, or incorporated in a combined video encoder-decoder
(CODEC).
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